The Manifesto

Intelligence is being solved.

This is not speculation. We are watching it happen. In 2023, GPT-4 passed the bar exam, the medical licensing exam, and scored in the 99th percentile on the GRE. In 2024, models began writing production code, conducting scientific research, and reasoning through novel problems they'd never seen. In 2025, the trajectory continues upward with no ceiling in sight.

The scaling laws hold. More compute, more data, more capability. Every doubling of resources produces measurable gains in reasoning, knowledge, and general problem-solving. We are not approaching a wall. We are approaching takeoff.

With sufficient intelligence and compute, the hardest problems become tractable:

Energy: Fusion is no longer 30 years away. Commonwealth Fusion Systems expects net energy by 2025 and grid power by 2030. Helion has a contract with Microsoft for 2028 delivery. Meanwhile, solar-plus-storage is already cheaper than coal in most markets, and AI-optimized grids could reduce transmission losses by 30-40%.

Disease: AlphaFold solved protein structure prediction—a 50-year grand challenge—in months. Drug discovery timelines are compressing from decades to years. AI diagnostics match or exceed specialist physicians in radiology, pathology, and dermatology. The bottleneck is no longer knowledge; it's deployment.

Material scarcity: Autonomous mining, AI-designed materials, robotic manufacturing. The marginal cost of physical goods approaches the cost of energy and raw materials. When energy is effectively free and labor is automated, scarcity becomes a choice, not a constraint.

Scientific discovery: AI systems are already generating novel hypotheses, designing experiments, and interpreting results. The rate of discovery is no longer limited by human cognitive bandwidth. We are entering an era where knowledge compounds faster than any individual can track.

This is the promise: a world of abundance. Where no one lacks what they need. Where human potential is unshackled from survival. Where the default state of existence is not scarcity, but sufficiency.

But here is what almost no one is saying:

We have the destination in sight. We can see the far shore. But we are standing at the edge of a chasm, and the bridge does not yet exist.

Consider: electricity was demonstrated in the 1830s. It took 100 years for most American homes to have it. The internet existed in 1969. Half the world still isn't connected. Technology does not distribute itself. Capability does not become access without intention, infrastructure, and often, struggle.

The gap is not technical. The gap is everything else:

Distribution: AI capability is concentrated in a handful of companies, in a handful of countries. The compute required for frontier models costs billions. Who decides who gets access? By what mechanism does capability become universal? The default answer is: slowly, unevenly, through market forces that favor those who already have resources.

Governance: No institution on Earth is prepared for AGI. Governments move in years; AI moves in months. International coordination on AI safety is nearly nonexistent. The entities building the most powerful systems are private companies with fiduciary duties to shareholders, not humanity.

Economics: Our entire economic system assumes human labor has value. When it doesn't, what happens? UBI is discussed but not deployed at scale anywhere. The transition from "most people work" to "most people don't" has no precedent and no plan.

Identity: For 10,000 years, humans have defined themselves by what they do, what they know, what they can provide. All three are about to be commoditized. The psychological disruption may be larger than the economic one.

Every conversation about AGI that skips these questions is incomplete. Every vision of abundance that doesn't account for the transition is fantasy.

We believe the bridge requires four pillars, built simultaneously:

1. Infrastructure for Distribution

Abundance means nothing if it cannot reach people. The systems that move value—economic, energetic, informational—must be rebuilt or replaced.

Energy infrastructure: We need grids that can handle distributed generation, storage at scale, and demand that may 10x as compute proliferates. Current grids were designed for centralized fossil fuel plants. They will not survive the transition without massive upgrade.

Compute infrastructure: Today, frontier AI requires data centers that cost billions. Democratizing intelligence means either radical cost reduction or public compute utilities. Whoever controls the GPUs controls the transition.

Physical distribution: Autonomous vehicles, drones, and robots can eliminate last-mile delivery costs. But the regulatory frameworks don't exist. The safety validation doesn't exist. The public trust doesn't exist.

Knowledge distribution: AI tutors could give every child on Earth access to elite education. AI doctors could bring specialist-level diagnosis to every village. The technology is nearly ready. The deployment is political.

2. New Institutions

The structures we have—governments, corporations, markets, universities—were designed for scarcity. They assume competition for limited resources. They will not survive contact with abundance unchanged.

What transforms: Universities become certification bodies rather than knowledge gatekeepers—why attend lectures when AI can teach better? Hospitals become places for complex procedures, not diagnosis—AI handles triage. Governments shift from service delivery to coordination, safety nets, and meaning infrastructure.

What collapses: Credentials as job requirements—why check degrees when AI can assess actual capability? Artificial scarcity in digital goods—when copying costs nothing, who pays? Geographic salary arbitrage—when work is remote and AI-augmented, location becomes irrelevant.

What must be built: New mechanisms for allocating resources when markets break down. New ways to coordinate millions of people without traditional employment. New frameworks for meaning and status that don't depend on economic productivity.

3. Human Adaptation

Identity, meaning, and purpose have been tied to work for all of recorded history. "What do you do?" is how we introduce ourselves. Work provides structure, community, status, and narrative. When work becomes optional, what fills the void?

This is not a problem AI solves. This is a problem humans must solve, and we have approximately one generation to figure it out.

Some will thrive—those who always wanted time to create, explore, connect. Some will spiral—those whose identity was entirely constructed around professional achievement. Most will struggle to find new foundations for self-worth.

We need new institutions for meaning: communities of practice, frameworks for contribution beyond labor, ways to matter that don't depend on being economically useful. We have not begun to build these.

4. Trust Infrastructure

In a world of synthetic media, AI-generated content, and automated manipulation, how do we know what's real? How do we coordinate when anyone can fake anything?

Verification: Cryptographic proof-of-humanity, provenance tracking for content, AI-assisted fact-checking at scale. The tools exist in prototype. They are not deployed.

Coordination: New mechanisms for collective decision-making that can handle millions of participants. Current democracy assumes informed voters with time to deliberate. Neither assumption holds.

Safety: Frameworks for ensuring AI systems remain aligned with human values as they become more capable. This is the alignment problem, and it is unsolved. We are building systems we do not fully understand or control.

What follows is not prediction. It is one plausible path, based on current trajectories. The future is not fixed. But having a map—even an imperfect one—is better than wandering blind.

2025–2026: The Capability Shock

AGI-level systems emerge—AI that can perform any cognitive task a human can, often better. Mass white-collar automation begins, starting with coding, legal research, financial analysis, and content creation. Public discourse shifts from "if" to "when" to "now."

Employment stays surprisingly stable initially. New jobs emerge as fast as old ones disappear—prompt engineering, AI training, human-in-the-loop oversight. But anxiety spikes. The psychological adjustment begins.

2027–2028: The Restructuring

UBI pilots launch in 10+ countries, driven by political necessity more than ideology. Education systems crack under pressure—why learn skills AI will have by graduation? First fully autonomous scientific discoveries published. Energy breakthroughs begin deployment at scale.

The "AI divide" becomes the defining inequality axis: those with access to frontier AI versus those without. Migration pressure intensifies as capabilities cluster geographically. Remote work becomes remote-AI-augmented work, then just AI work with human oversight.

2029–2030: The Sorting

Clear winners and losers emerge. Some nations achieve local abundance—basic needs met for all citizens through AI-driven production. Others fall behind, lacking compute, infrastructure, or governance capacity. The gap widens.

New political movements form around abundance and scarcity, transcending traditional left-right divides. "Post-work" becomes a mainstream political identity. The meaning crisis peaks—rates of depression, anxiety, and purposelessness reach historical highs even as material conditions improve.

2031–2035: The New Normal

For leading regions, post-scarcity becomes real for basic needs. Food, shelter, healthcare, education, energy—effectively free. Work becomes optional for survival, though many still choose it for status, meaning, or preference.

New social structures stabilize—or don't. Some communities find new foundations for meaning and connection. Others fragment. The variance in human flourishing increases even as the floor rises.

The Variable: What We Choose

None of this is inevitable. Every transition point is a choice. The path depends on who builds what, who governs how, and whether we figure out the human part before the technical part outpaces us.

Abundance is possible. So is dystopia. The technology doesn't choose. We do.

This manifesto is not a prediction. It is not a plan. It is a starting point.

We don't have all the answers. We're not sure anyone does. But we believe the questions matter, and that asking them clearly is the first step toward answering them.

We are mapping the bridge.

This is an invitation to think seriously about the transition. To move past the hype and the fear, past the utopian visions and the doom prophecies, into the practical work of figuring out how we actually get from here to there.

The future is not something that happens to us. It is something we build. The bridge will not construct itself.